Overview

Dataset statistics

Number of variables15
Number of observations7208
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory844.8 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 13 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T6 is highly correlated with TIME and 13 other fieldsHigh correlation
T7 is highly correlated with TIME and 13 other fieldsHigh correlation
T8 is highly correlated with TIME and 13 other fieldsHigh correlation
T9 is highly correlated with TIME and 13 other fieldsHigh correlation
T10 is highly correlated with TIME and 13 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 1252 (17.4%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:26:48.535421
Analysis finished2022-11-11 03:27:00.635232
Duration12.1 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.2916667
Minimum0
Maximum600.5833333
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:00.662082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.02916667
Q1150.1458333
median300.2916667
Q3450.4375
95-th percentile570.5541667
Maximum600.5833333
Range600.5833333
Interquartile range (IQR)300.2916667

Descriptive statistics

Standard deviation173.4095586
Coefficient of variation (CV)0.5774704323
Kurtosis-1.2
Mean300.2916667
Median Absolute Deviation (MAD)150.1666667
Skewness-3.045988314 × 10-16
Sum2164502.333
Variance30070.875
MonotonicityStrictly increasing
2022-11-11T11:27:00.719223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
403.51
 
< 0.1%
4011
 
< 0.1%
400.91666671
 
< 0.1%
400.83333331
 
< 0.1%
400.751
 
< 0.1%
400.66666671
 
< 0.1%
400.58333331
 
< 0.1%
400.51
 
< 0.1%
400.41666671
 
< 0.1%
Other values (7198)7198
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
600.58333331
< 0.1%
600.51
< 0.1%
600.41666671
< 0.1%
600.33333331
< 0.1%
600.251
< 0.1%
600.16666671
< 0.1%
600.08333331
< 0.1%
6001
< 0.1%
599.91666671
< 0.1%
599.83333331
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10852.73557
Minimum0
Maximum20001
Zeros1252
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:00.772730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18000
median12001
Q315999
95-th percentile19999
Maximum20001
Range20001
Interquartile range (IQR)7999

Descriptive statistics

Standard deviation5904.420783
Coefficient of variation (CV)0.5440490782
Kurtosis-0.4611655028
Mean10852.73557
Median Absolute Deviation (MAD)3998
Skewness-0.6607969757
Sum78226518
Variance34862184.79
MonotonicityNot monotonic
2022-11-11T11:27:00.817580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
120011288
17.9%
01252
17.4%
15999987
13.7%
14000876
12.2%
9998765
10.6%
8000705
9.8%
18000430
 
6.0%
20001299
 
4.1%
7999242
 
3.4%
13999240
 
3.3%
Other values (7)124
 
1.7%
ValueCountFrequency (%)
01252
17.4%
34871
 
< 0.1%
7999242
 
3.4%
8000705
9.8%
97051
 
< 0.1%
9998765
10.6%
102251
 
< 0.1%
120011288
17.9%
13999240
 
3.3%
14000876
12.2%
ValueCountFrequency (%)
20001299
 
4.1%
19999105
 
1.5%
186491
 
< 0.1%
18000430
 
6.0%
1799814
 
0.2%
15999987
13.7%
159971
 
< 0.1%
14000876
12.2%
13999240
 
3.3%
120011288
17.9%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct67
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.20546615
Minimum22.6
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:00.872469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.6
5-th percentile23.5
Q123.6
median24
Q324.7
95-th percentile25.7
Maximum25.9
Range3.3
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.7020248256
Coefficient of variation (CV)0.02900273935
Kurtosis-0.4418340712
Mean24.20546615
Median Absolute Deviation (MAD)0.5
Skewness0.656424249
Sum174473
Variance0.4928388558
MonotonicityNot monotonic
2022-11-11T11:27:00.930086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.51359
18.9%
24.6973
13.5%
24956
13.3%
23.6757
10.5%
24.7469
 
6.5%
25.2406
 
5.6%
23.9405
 
5.6%
25320
 
4.4%
23.7306
 
4.2%
25.9260
 
3.6%
Other values (57)997
13.8%
ValueCountFrequency (%)
22.68
 
0.1%
22.651
 
< 0.1%
22.73
 
< 0.1%
22.752
 
< 0.1%
22.82
 
< 0.1%
22.851
 
< 0.1%
22.92
 
< 0.1%
22.951
 
< 0.1%
2337
0.5%
23.051
 
< 0.1%
ValueCountFrequency (%)
25.9260
3.6%
25.852
 
< 0.1%
25.884
 
1.2%
25.752
 
< 0.1%
25.716
 
0.2%
25.652
 
< 0.1%
25.620
 
0.3%
25.552
 
< 0.1%
25.518
 
0.2%
25.452
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.98827691
Minimum22.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:00.983592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.5
Q122.7
median23
Q323.2
95-th percentile23.5
Maximum23.5
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3084669387
Coefficient of variation (CV)0.0134184454
Kurtosis-0.9771918
Mean22.98827691
Median Absolute Deviation (MAD)0.2
Skewness0.3169364304
Sum165699.5
Variance0.09515185227
MonotonicityNot monotonic
2022-11-11T11:27:01.023217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23.51208
16.8%
23.11068
14.8%
22.8881
12.2%
22.7807
11.2%
22.9790
11.0%
22.6736
10.2%
23651
9.0%
23.3378
 
5.2%
22.5368
 
5.1%
23.2307
 
4.3%
ValueCountFrequency (%)
22.5368
 
5.1%
22.6736
10.2%
22.7807
11.2%
22.8881
12.2%
22.9790
11.0%
23651
9.0%
23.11068
14.8%
23.2307
 
4.3%
23.3378
 
5.2%
23.414
 
0.2%
ValueCountFrequency (%)
23.51208
16.8%
23.414
 
0.2%
23.3378
 
5.2%
23.2307
 
4.3%
23.11068
14.8%
23651
9.0%
22.9790
11.0%
22.8881
12.2%
22.7807
11.2%
22.6736
10.2%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.97715039
Minimum22.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.065236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.6
Q122.8
median22.9
Q323.1
95-th percentile23.5
Maximum23.5
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.3018987755
Coefficient of variation (CV)0.01313908689
Kurtosis-0.7857693939
Mean22.97715039
Median Absolute Deviation (MAD)0.2
Skewness0.4323706227
Sum165619.3
Variance0.09114287068
MonotonicityNot monotonic
2022-11-11T11:27:01.105128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23.51212
16.8%
22.81027
14.2%
23994
13.8%
22.9868
12.0%
23.1867
12.0%
22.6755
10.5%
22.7667
9.3%
22.5355
 
4.9%
23.2255
 
3.5%
23.3198
 
2.7%
ValueCountFrequency (%)
22.5355
 
4.9%
22.6755
10.5%
22.7667
9.3%
22.81027
14.2%
22.9868
12.0%
23994
13.8%
23.1867
12.0%
23.2255
 
3.5%
23.3198
 
2.7%
23.410
 
0.1%
ValueCountFrequency (%)
23.51212
16.8%
23.410
 
0.1%
23.3198
 
2.7%
23.2255
 
3.5%
23.1867
12.0%
23994
13.8%
22.9868
12.0%
22.81027
14.2%
22.7667
9.3%
22.6755
10.5%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2954495
Minimum22.6
Maximum23.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.144994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.6
5-th percentile22.7
Q123
median23.3
Q323.6
95-th percentile23.9
Maximum23.9
Range1.3
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.371286384
Coefficient of variation (CV)0.01593815067
Kurtosis-1.086728163
Mean23.2954495
Median Absolute Deviation (MAD)0.3
Skewness-0.08999102759
Sum167913.6
Variance0.137853579
MonotonicityNot monotonic
2022-11-11T11:27:01.190482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
23.2786
10.9%
23.6749
10.4%
22.8747
10.4%
23.4683
9.5%
23.5623
8.6%
23.9570
7.9%
23.3558
7.7%
22.9542
7.5%
23530
7.4%
23.7451
6.3%
Other values (4)969
13.4%
ValueCountFrequency (%)
22.6210
 
2.9%
22.7247
 
3.4%
22.8747
10.4%
22.9542
7.5%
23530
7.4%
23.1151
 
2.1%
23.2786
10.9%
23.3558
7.7%
23.4683
9.5%
23.5623
8.6%
ValueCountFrequency (%)
23.9570
7.9%
23.8361
5.0%
23.7451
6.3%
23.6749
10.4%
23.5623
8.6%
23.4683
9.5%
23.3558
7.7%
23.2786
10.9%
23.1151
 
2.1%
23530
7.4%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.23963651
Minimum22.7
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.298630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile22.8
Q123
median23.2
Q323.5
95-th percentile23.7
Maximum23.7
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3158954025
Coefficient of variation (CV)0.01359295798
Kurtosis-1.284674579
Mean23.23963651
Median Absolute Deviation (MAD)0.3
Skewness0.04708470386
Sum167511.3
Variance0.09978990532
MonotonicityNot monotonic
2022-11-11T11:27:01.338371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23.71112
15.4%
23973
13.5%
23.2791
11.0%
23.4767
10.6%
22.9708
9.8%
23.6686
9.5%
22.8644
8.9%
23.1468
6.5%
23.3454
6.3%
23.5338
 
4.7%
ValueCountFrequency (%)
22.7267
 
3.7%
22.8644
8.9%
22.9708
9.8%
23973
13.5%
23.1468
6.5%
23.2791
11.0%
23.3454
6.3%
23.4767
10.6%
23.5338
 
4.7%
23.6686
9.5%
ValueCountFrequency (%)
23.71112
15.4%
23.6686
9.5%
23.5338
 
4.7%
23.4767
10.6%
23.3454
6.3%
23.2791
11.0%
23.1468
6.5%
23973
13.5%
22.9708
9.8%
22.8644
8.9%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.18446171
Minimum23
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.377243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q123
median23.1
Q323.3
95-th percentile23.5
Maximum23.6
Range0.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.19529633
Coefficient of variation (CV)0.008423586988
Kurtosis-0.8804049008
Mean23.18446171
Median Absolute Deviation (MAD)0.1
Skewness0.6816809507
Sum167113.6
Variance0.03814065651
MonotonicityNot monotonic
2022-11-11T11:27:01.415407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
232858
39.7%
23.21149
15.9%
23.1889
 
12.3%
23.5798
 
11.1%
23.4620
 
8.6%
23.3575
 
8.0%
23.6319
 
4.4%
ValueCountFrequency (%)
232858
39.7%
23.1889
 
12.3%
23.21149
15.9%
23.3575
 
8.0%
23.4620
 
8.6%
23.5798
 
11.1%
23.6319
 
4.4%
ValueCountFrequency (%)
23.6319
 
4.4%
23.5798
 
11.1%
23.4620
 
8.6%
23.3575
 
8.0%
23.21149
15.9%
23.1889
 
12.3%
232858
39.7%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.88333796
Minimum22.4
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.458262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.4
5-th percentile22.5
Q122.6
median22.8
Q323
95-th percentile23.5
Maximum23.5
Range1.1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3268157035
Coefficient of variation (CV)0.01428181955
Kurtosis-0.4564838717
Mean22.88333796
Median Absolute Deviation (MAD)0.2
Skewness0.6864581112
Sum164943.1
Variance0.1068085041
MonotonicityNot monotonic
2022-11-11T11:27:01.498028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
23.51107
15.4%
22.91090
15.1%
22.71022
14.2%
22.8884
12.3%
22.5768
10.7%
23756
10.5%
22.6733
10.2%
23.1379
 
5.3%
22.4344
 
4.8%
23.481
 
1.1%
Other values (2)44
 
0.6%
ValueCountFrequency (%)
22.4344
 
4.8%
22.5768
10.7%
22.6733
10.2%
22.71022
14.2%
22.8884
12.3%
22.91090
15.1%
23756
10.5%
23.1379
 
5.3%
23.219
 
0.3%
23.325
 
0.3%
ValueCountFrequency (%)
23.51107
15.4%
23.481
 
1.1%
23.325
 
0.3%
23.219
 
0.3%
23.1379
 
5.3%
23756
10.5%
22.91090
15.1%
22.8884
12.3%
22.71022
14.2%
22.6733
10.2%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.03372642
Minimum22.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.540833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.6
Q122.8
median23
Q323.2
95-th percentile23.5
Maximum23.5
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3010990249
Coefficient of variation (CV)0.01307209348
Kurtosis-1.12030583
Mean23.03372642
Median Absolute Deviation (MAD)0.2
Skewness0.1714394852
Sum166027.1
Variance0.0906606228
MonotonicityNot monotonic
2022-11-11T11:27:01.581581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22.81416
19.6%
23.51215
16.9%
231050
14.6%
23.21035
14.4%
22.7637
8.8%
22.6453
 
6.3%
23.1418
 
5.8%
23.4323
 
4.5%
22.9265
 
3.7%
22.5224
 
3.1%
ValueCountFrequency (%)
22.5224
 
3.1%
22.6453
 
6.3%
22.7637
8.8%
22.81416
19.6%
22.9265
 
3.7%
231050
14.6%
23.1418
 
5.8%
23.21035
14.4%
23.3172
 
2.4%
23.4323
 
4.5%
ValueCountFrequency (%)
23.51215
16.9%
23.4323
 
4.5%
23.3172
 
2.4%
23.21035
14.4%
23.1418
 
5.8%
231050
14.6%
22.9265
 
3.7%
22.81416
19.6%
22.7637
8.8%
22.6453
 
6.3%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct83
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.4466773
Minimum23
Maximum27.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.633408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile24
Q124.4
median25.8
Q326.25
95-th percentile26.75
Maximum27.1
Range4.1
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation0.9845389997
Coefficient of variation (CV)0.03869027724
Kurtosis-1.143562794
Mean25.4466773
Median Absolute Deviation (MAD)0.6
Skewness-0.3753473281
Sum183419.65
Variance0.969317042
MonotonicityNot monotonic
2022-11-11T11:27:01.688170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26433
 
6.0%
26.1431
 
6.0%
26.2413
 
5.7%
26.3404
 
5.6%
24.2371
 
5.1%
25.2346
 
4.8%
26.4300
 
4.2%
24286
 
4.0%
24.4285
 
4.0%
25.9261
 
3.6%
Other values (73)3678
51.0%
ValueCountFrequency (%)
2318
 
0.2%
23.051
 
< 0.1%
23.15
 
0.1%
23.151
 
< 0.1%
23.252
0.7%
23.251
 
< 0.1%
23.34
 
0.1%
23.351
 
< 0.1%
23.450
0.7%
23.451
 
< 0.1%
ValueCountFrequency (%)
27.115
 
0.2%
27.052
 
< 0.1%
27100
1.4%
26.9512
 
0.2%
26.973
 
1.0%
26.8513
 
0.2%
26.8145
2.0%
26.7518
 
0.2%
26.7194
2.7%
26.6518
 
0.2%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.88440622
Minimum22.2
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.745239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.2
5-th percentile22.8
Q123.1
median23.6
Q327.8
95-th percentile28.2
Maximum28.6
Range6.4
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.274441191
Coefficient of variation (CV)0.09140025973
Kurtosis-1.545922894
Mean24.88440622
Median Absolute Deviation (MAD)0.6
Skewness0.5968554918
Sum179366.8
Variance5.173082733
MonotonicityNot monotonic
2022-11-11T11:27:01.801224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.1757
 
10.5%
23.6576
 
8.0%
23564
 
7.8%
23.2410
 
5.7%
27.9358
 
5.0%
22.9351
 
4.9%
23.7350
 
4.9%
28.1345
 
4.8%
28.2343
 
4.8%
28331
 
4.6%
Other values (55)2823
39.2%
ValueCountFrequency (%)
22.213
 
0.2%
22.340
 
0.6%
22.438
 
0.5%
22.525
 
0.3%
22.647
 
0.7%
22.7166
 
2.3%
22.8281
 
3.9%
22.9351
4.9%
23564
7.8%
23.1757
10.5%
ValueCountFrequency (%)
28.634
 
0.5%
28.536
 
0.5%
28.482
 
1.1%
28.3177
2.5%
28.2343
4.8%
28.1345
4.8%
28331
4.6%
27.9358
5.0%
27.8312
4.3%
27.7156
2.2%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.19372919
Minimum22.2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.853070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.2
5-th percentile22.5
Q122.9
median23.3
Q323.5
95-th percentile23.8
Maximum24
Range1.8
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4069188901
Coefficient of variation (CV)0.01754434946
Kurtosis-0.8677133929
Mean23.19372919
Median Absolute Deviation (MAD)0.3
Skewness-0.2120036924
Sum167180.4
Variance0.1655829831
MonotonicityNot monotonic
2022-11-11T11:27:01.899136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
23.4815
11.3%
23.3805
11.2%
22.9719
10.0%
23.2639
8.9%
22.8608
8.4%
23.5561
 
7.8%
23.7486
 
6.7%
23.6422
 
5.9%
22.6373
 
5.2%
22.7354
 
4.9%
Other values (9)1426
19.8%
ValueCountFrequency (%)
22.29
 
0.1%
22.3124
 
1.7%
22.471
 
1.0%
22.5234
 
3.2%
22.6373
5.2%
22.7354
4.9%
22.8608
8.4%
22.9719
10.0%
23140
 
1.9%
23.1252
 
3.5%
ValueCountFrequency (%)
2424
 
0.3%
23.9229
 
3.2%
23.8343
4.8%
23.7486
6.7%
23.6422
5.9%
23.5561
7.8%
23.4815
11.3%
23.3805
11.2%
23.2639
8.9%
23.1252
 
3.5%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.08077137
Minimum22.3
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:01.946695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.3
5-th percentile22.7
Q122.9
median23.1
Q323.3
95-th percentile23.6
Maximum23.8
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2838991214
Coefficient of variation (CV)0.01230024408
Kurtosis-0.2673644729
Mean23.08077137
Median Absolute Deviation (MAD)0.2
Skewness0.2152047444
Sum166366.2
Variance0.08059871114
MonotonicityNot monotonic
2022-11-11T11:27:01.988296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
231197
16.6%
23.11114
15.5%
22.8909
12.6%
23.3802
11.1%
22.7649
9.0%
23.2631
8.8%
22.9507
7.0%
23.4386
 
5.4%
23.6383
 
5.3%
23.5225
 
3.1%
Other values (6)405
 
5.6%
ValueCountFrequency (%)
22.317
 
0.2%
22.482
 
1.1%
22.552
 
0.7%
22.655
 
0.8%
22.7649
9.0%
22.8909
12.6%
22.9507
7.0%
231197
16.6%
23.11114
15.5%
23.2631
8.8%
ValueCountFrequency (%)
23.843
 
0.6%
23.7156
 
2.2%
23.6383
 
5.3%
23.5225
 
3.1%
23.4386
 
5.4%
23.3802
11.1%
23.2631
8.8%
23.11114
15.5%
231197
16.6%
22.9507
7.0%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct260
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.9768313
Minimum3.2
Maximum60.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:02.042122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile16.4
Q124.8
median29.8
Q338.9
95-th percentile57.2
Maximum60.1
Range56.9
Interquartile range (IQR)14.1

Descriptive statistics

Standard deviation11.74408457
Coefficient of variation (CV)0.3672685533
Kurtosis-0.1836187747
Mean31.9768313
Median Absolute Deviation (MAD)8.9
Skewness0.503827316
Sum230489
Variance137.9235224
MonotonicityNot monotonic
2022-11-11T11:27:02.096937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26502
 
7.0%
39.7433
 
6.0%
29.4336
 
4.7%
29.8319
 
4.4%
30.6297
 
4.1%
16.9293
 
4.1%
28.2239
 
3.3%
38.7221
 
3.1%
51178
 
2.5%
16.8166
 
2.3%
Other values (250)4224
58.6%
ValueCountFrequency (%)
3.28
0.1%
4.051
 
< 0.1%
4.93
 
< 0.1%
5.752
 
< 0.1%
6.62
 
< 0.1%
7.451
 
< 0.1%
8.32
 
< 0.1%
8.46
0.1%
9.151
 
< 0.1%
9.251
 
< 0.1%
ValueCountFrequency (%)
60.156
0.8%
59.744
 
0.6%
59.6138
1.9%
59.251
 
< 0.1%
59.222
 
0.3%
58.929
 
0.4%
58.851
 
< 0.1%
58.453
 
0.7%
58.47
 
0.1%
58.051
 
< 0.1%

Interactions

2022-11-11T11:26:59.684743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:48.979421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.713578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.529831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.346406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.057621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.824897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.561717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.328192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.125583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.852110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.624974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.323089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.110692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.936534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.731815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.029317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.764336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.579114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.394245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.104463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.874045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.608559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.376960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.173550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.899949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.670979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.370783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.160434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.986638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.781731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.082139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.874983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.631936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.446070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.154401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.926668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.723327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.428859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.226372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.950778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.720812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.422608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.214237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.039481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.831131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.134031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.927554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.684825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.496999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.204233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.977463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.771322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.480609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.276168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.000610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.769695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.472511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.268050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.091306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.875002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.182447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.975749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.732831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.542844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.249673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.024361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.816171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.527111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.322020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.045458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.814237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.518135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.315554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.137883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.921300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.228971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.023995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.780670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.587693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.294466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.071250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.860269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.573768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.367865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.090110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.858694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.628815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.363393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.185722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.968983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.277848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.075766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.830925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.636165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.341484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.120044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.907692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.623224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.417526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.138553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.905758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.678158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.414282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.235554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.014833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.324034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.123948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.879815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.681309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.451684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.165962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.951509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.670135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.463510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.183179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.950686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.724492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.462708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.284389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.063150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.373161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.176770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.931683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.729147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.498926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.217437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.999403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.719931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.513294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.231018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.997528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.773295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.513536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.335218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.110255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.421014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.226761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.981230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.776294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.545324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.266414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.045686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.767966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.559891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.343988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.043419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.821134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.562425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.384883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.219718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.469018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.274499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.028561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.821465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.589226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.312312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.090535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.814771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.606942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.388836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.088938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.866405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.611085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.432758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.265564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.514863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.322337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.141147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.866314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.633474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.359411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.134436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.861363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.653742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.433685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.132790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.912731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.658603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.480744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.313403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.563699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.373228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.192339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.913217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.680634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.408234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.181839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.910845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.702632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.480704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.179632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.961618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.708487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.530541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.363332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.614859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.426567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.245746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.963212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.730457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.461056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.232667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.027273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.753742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.529504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.228709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.012446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.761721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.583444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:00.412996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:49.665688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:50.480251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:51.298568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.011775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:52.779410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:53.513878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:54.282618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.079144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:55.804947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:56.579161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:57.277186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.062858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:58.814528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:59.634911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:27:02.154018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:27:02.291307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:27:02.370445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:27:02.448122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:27:02.526347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:27:00.491730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:27:00.598439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000022.6022.622.722.622.723.222.622.623.022.322.322.43.20
10.083333022.6022.622.722.622.723.222.622.623.022.322.222.43.20
20.166667022.6022.622.722.622.723.222.622.623.022.322.322.43.20
30.250000022.6022.622.722.622.723.222.622.623.022.322.322.43.20
40.333333022.6022.622.722.622.723.222.622.623.022.322.322.43.20
50.416667022.6022.622.722.622.723.222.622.623.022.322.222.43.20
60.5000001200122.6022.622.722.622.723.222.622.623.022.322.322.43.20
70.5833331200122.6022.622.722.622.723.222.622.623.022.322.322.43.20
80.6666671200122.6522.622.722.622.723.222.622.623.022.322.222.44.05
90.7500001200122.7022.622.722.622.723.222.622.623.022.322.222.44.90

Last rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
7198599.833333023.523.523.523.623.623.623.523.525.123.022.922.729.8
7199599.916667023.523.523.523.623.623.623.523.525.123.022.922.729.8
7200600.000000023.523.523.523.623.623.623.523.525.123.022.922.729.8
7201600.083333023.523.523.523.623.623.623.523.525.123.022.922.729.8
7202600.166667023.523.523.523.623.623.623.523.525.123.022.922.729.8
7203600.250000023.523.523.523.623.623.623.523.525.123.022.922.729.8
7204600.333333023.523.523.523.623.623.623.523.525.123.022.922.729.8
7205600.416667023.523.523.523.623.623.623.523.525.123.022.922.729.8
7206600.500000023.523.523.523.623.623.623.523.525.123.022.922.729.8
7207600.583333023.523.523.523.623.623.623.523.525.123.022.922.729.8